Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 17, issue 9
Articles | Volume 17, issue 9
https://doi.org/10.5194/nhess-17-1683-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-17-1683-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 17, issue 9
Research article
 | 
29 Sep 2017
Research article |  | 29 Sep 2017

Multi-variable flood damage modelling with limited data using supervised learning approaches

Dennis Wagenaar, Jurjen de Jong, and Laurens M. Bouwer

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Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
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Damage of natural hazards: assessment and mitigation
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